Perceptual loss [1]} is widely used to calculate the similarity of semantic and perceptual quality between images. It measures the semantic differences via a pre-trained image classification network (such as VGG), where the extracted features are less relevant to our generation task.
Meanwhile, the teacher discriminator has a task-oriented ability to distinguish the authenticity of images.
Following the work [2]}, we employ the teacher discriminator to replace the pre-trained classification model to extract the features of the images for calculating the perceptual loss.
Furthermore, the intermediate feature map of the generator has rich information for generating real-like fake images, and we also add them to the calculation of perceptual loss.
In this way, the similarity metric is summarized as:
\(d(\cdot , \cdot )=\gamma _m MSE(\cdot , \cdot ) + \gamma _t Texture(\cdot , \cdot ),\) 
